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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g4JTHJ-IATho"
      },
      "source": [
        "# **Fine-tuning for Audio Classification with 🤗 Transformers**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-CI6ON8AMcc"
      },
      "source": [
        "This notebook shows how to fine-tune multi-lingual pretrained speech models for Automatic Speech Recognition."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pdcMxVGEA9Cd"
      },
      "source": [
        "This notebook is built to run on the **Keyword Spotting** subset of the [SUPERB dataset](https://huggingface.co/datasets/superb) with any speech model checkpoint from the [Model Hub](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&sort=downloads) as long as that model has a version with a Sequence Classification head (e.g. [Wav2Vec2ForSequenceClassification](https://huggingface.co/transformers/model_doc/wav2vec2.html#wav2vec2forsequenceclassification)). \n",
        "\n",
        "Depending on the model and the GPU you are using, you might need to adjust the batch size to avoid out-of-memory errors. Set those two parameters, then the rest of the notebook should run smoothly:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5WMEawzyCEyG"
      },
      "source": [
        "model_checkpoint = \"facebook/wav2vec2-base\"\n",
        "batch_size = 32"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlArTG8KChJf"
      },
      "source": [
        "Before we start, let's install both `datasets` and `transformers` from master. Also, we need the `librosa` package to load audio files."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L1532RVbJgQV"
      },
      "source": [
        "%%capture\n",
        "!pip install datasets==1.14\n",
        "!pip install transformers==4.11.3\n",
        "!pip install librosa"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "snZ1tmaOC412"
      },
      "source": [
        "If you're opening this notebook locally, make sure your environment has an install from the last version of those libraries.\n",
        "\n",
        "To be able to share your model with the community and generate results like the one shown in the picture below via the inference API, there are a few more steps to follow.\n",
        "\n",
        "First you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!) then execute the following cell and input your username and password:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bkpk_JPlCww8"
      },
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJ0986jTDZRC"
      },
      "source": [
        "\n",
        "Then you need to install Git-LFS to upload your model checkpoints:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7JEhLjDDcVd"
      },
      "source": [
        "%%capture\n",
        "!apt install git-lfs"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XalxdrirGkLl"
      },
      "source": [
        "## Fine-tuning a model on an audio classification task"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cnRWZSq0GRRh"
      },
      "source": [
        "In this notebook, we will see how to fine-tune one of the [🤗 Transformers](https://github.com/huggingface/transformers) acoustic models to a Keyword Spotting task of the [SUPERB Benchmark](https://superbbenchmark.org/)\n",
        "\n",
        "Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. SUPERB uses the widely used Speech Commands dataset v1.0 for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lu8Od7EgFLwe"
      },
      "source": [
        "<img src=\"\" alt=\"drawing\" width=\"400\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcE455KaG687"
      },
      "source": [
        "### Loading the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RD_G2KJgG_bU"
      },
      "source": [
        "We will use the [🤗 Datasets](https://github.com/huggingface/datasets) library to download the data and get the Accuracy metric we need to use for evaluation. This can be easily done with the functions `load_dataset` and `load_metric`.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhVQU42BLd3p"
      },
      "source": [
        "from datasets import load_dataset, load_metric"
      ],
      "execution_count": 5,
      "outputs": []
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        },
        "id": "8UGse36eLeeb",
        "outputId": "4c3a0a8d-1afe-4655-e22f-c579407685e6"
      },
      "source": [
        "dataset = load_dataset(\"superb\", \"ks\")\n",
        "metric = load_metric(\"accuracy\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4b35a346a5624e0d805105529763b59f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/7.54k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a0a4c103e9dd46fbb0cfbb716cc2bdc3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/7.02k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset superb/ks (download: 1.45 GiB, generated: 9.64 MiB, post-processed: Unknown size, total: 1.46 GiB) to /root/.cache/huggingface/datasets/superb/ks/1.9.0/ce836692657f82230c16b3bbcb93eaacdbfd7de4def3be90016f112d68683481...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1633b0a206084a0f831b833d510d3f99",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "1e53f3247b6141c189bdb6e69284eafc",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7497304b35e74136bf8b310cb74c7dd0",
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              "version_major": 2
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8bb4a780705b4af38ea6a78d8c6eed8a",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "be41cb2898184c35b7d757daf1438e8c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6ca5faf5fc29476e99c5dc99cbd3d939",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3a49119dc3c54e9ca2aacb270b3ea94f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset superb downloaded and prepared to /root/.cache/huggingface/datasets/superb/ks/1.9.0/ce836692657f82230c16b3bbcb93eaacdbfd7de4def3be90016f112d68683481. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c138fc2a063c40a8bee34eb2925acc0a",
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "5b61e0b2d3174c2faeb828cbfa572d26",
              "version_minor": 0,
              "version_major": 2
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r8mTmFdlHOmN"
      },
      "source": [
        "The `dataset` object itself is a [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasetdict), which contains one key for the training, validation and test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7tjOWPQYLq4u",
        "outputId": "3bc4c860-b807-48c9-a2f6-7d8a68f0ca70"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['file', 'audio', 'label'],\n",
              "        num_rows: 51094\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['file', 'audio', 'label'],\n",
              "        num_rows: 6798\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['file', 'audio', 'label'],\n",
              "        num_rows: 3081\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nfPPNjthI3u2"
      },
      "source": [
        "To access an actual element, you need to select a split first, then give an index:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BujWoSgyMQlw",
        "outputId": "bb0e7e31-e0a1-4d3b-b28d-b50cb48d19d2"
      },
      "source": [
        "dataset[\"test\"][1000]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'audio': {'array': array([-1.2207031e-04,  3.0517578e-05,  1.8310547e-04, ...,\n",
              "         -4.8828125e-04, -5.4931641e-04, -3.3569336e-04], dtype=float32),\n",
              "  'path': '/root/.cache/huggingface/datasets/downloads/extracted/94c69f12cc18539f842ccec832347d0f85a3424c14bc47bd63902105ba1b2391/go/e41a903b_nohash_4.wav',\n",
              "  'sampling_rate': 16000},\n",
              " 'file': '/root/.cache/huggingface/datasets/downloads/extracted/94c69f12cc18539f842ccec832347d0f85a3424c14bc47bd63902105ba1b2391/go/e41a903b_nohash_4.wav',\n",
              " 'label': 9}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f8GpxwfnJCkF"
      },
      "source": [
        "As you can see, the `label` field is not an actual string label. By default the `ClassLabel` fields are encoded into integers for convenience:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n33LZz_ZMU3o",
        "outputId": "e2526603-c8a6-4103-8d29-95ef93f396f0"
      },
      "source": [
        "dataset[\"train\"].features[\"label\"]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ClassLabel(num_classes=12, names=['yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go', '_silence_', '_unknown_'], names_file=None, id=None)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5LdpDtScLgeD"
      },
      "source": [
        "Let's create an `id2label` dictionary to decode them back to strings and see what they are. The inverse `label2id` will be useful too, when we load the model later."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "UuyXDtQqNUZW",
        "outputId": "76fc874f-d1a1-474a-aafd-e9aa2f514098"
      },
      "source": [
        "labels = dataset[\"train\"].features[\"label\"].names\n",
        "label2id, id2label = dict(), dict()\n",
        "for i, label in enumerate(labels):\n",
        "    label2id[label] = str(i)\n",
        "    id2label[str(i)] = label\n",
        "\n",
        "id2label[\"9\"]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'go'"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GD7wBXeIltx0"
      },
      "source": [
        "`Wav2Vec2` expects the input in the format of a 1-dimensional array of 16 kHz. This means that the audio file has to be loaded and resampled.\n",
        "\n",
        " Thankfully, `datasets` does this automatically when calling the column `audio`. Let try it out. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKIaV1ndl0j6",
        "outputId": "ce504d58-05b7-4106-edc6-7c9c76f5e650"
      },
      "source": [
        "dataset[\"test\"][1000][\"audio\"]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'array': array([-1.2207031e-04,  3.0517578e-05,  1.8310547e-04, ...,\n",
              "        -4.8828125e-04, -5.4931641e-04, -3.3569336e-04], dtype=float32),\n",
              " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/94c69f12cc18539f842ccec832347d0f85a3424c14bc47bd63902105ba1b2391/go/e41a903b_nohash_4.wav',\n",
              " 'sampling_rate': 16000}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eEfxyaatlzU-"
      },
      "source": [
        "We can see that the audio file has automatically been loaded. This is thanks to the new [`\"Audio\"` feature](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=audio#datasets.Audio) introduced in `datasets == 1.13.3`, which loads and resamples audio files on-the-fly upon calling.\n",
        "\n",
        "The sampling rate is set to 16kHz which is what `Wav2Vec2` expects as an input."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSvqeGihJvp4"
      },
      "source": [
        "To get a sense of what the commands sound like, the following snippet will render \n",
        "some audio examples picked randomly from the dataset. \n",
        "\n",
        "**Note**: *You can run the following cell a couple of times to listen to different audio samples.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "RI0kb4woNvuU",
        "outputId": "2ebe6acf-4a98-4d89-ebbf-b6546ff2e516"
      },
      "source": [
        "import random\n",
        "from IPython.display import Audio, display\n",
        "\n",
        "for _ in range(5):\n",
        "    rand_idx = random.randint(0, len(dataset[\"train\"])-1)\n",
        "    example = dataset[\"train\"][rand_idx]\n",
        "    audio = example[\"audio\"]\n",
        "\n",
        "    print(f'Label: {id2label[str(example[\"label\"])]}')\n",
        "    print(f'Shape: {audio[\"array\"].shape}, sampling rate: {audio[\"sampling_rate\"]}')\n",
        "    display(Audio(audio[\"array\"], rate=audio[\"sampling_rate\"]))\n",
        "    print()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label: go\n",
            "Shape: (16000,), sampling rate: 16000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Label: down\n",
            "Shape: (16000,), sampling rate: 16000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Label: go\n",
            "Shape: (16000,), sampling rate: 16000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Label: _unknown_\n",
            "Shape: (15604,), sampling rate: 16000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37K_0FAQoNRM"
      },
      "source": [
        "If you run the cell a couple of times, you'll see that despite slight variations in length, most of the samples are about 1 second long (`duration = audio_length / sampling_rate`). So we can safely truncate and pad the samples to `16000`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zxoikSOjs0K"
      },
      "source": [
        "### Preprocessing the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WTupOU88p1lK"
      },
      "source": [
        "Before we can feed those audio clips to our model, we need to preprocess them. This is done by a 🤗 Transformers `FeatureExtractor` which will normalize the inputs and put them in a format the model expects, as well as generate the other inputs that the model requires.\n",
        "\n",
        "To do all of this, we instantiate our feature extractor with the `AutoFeatureExtractor.from_pretrained` method, which will ensure that we get a preprocessor that corresponds to the model architecture we want to use."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288,
          "referenced_widgets": [
            "2fafd1b4c6c5483f9922bef1395dba65",
            "e6548e0782514f58829c032e751d96be",
            "ddf5c88d13264d468753cf722df5159b",
            "a7ce0b3ccc5d49b0871b6deb30fb4e03",
            "d211c0a9b6fd4706a3cd4259070337d4",
            "0c5d6edfe85e4b6bbef0fd2e4a4a9da0",
            "716a18a372244e0d9d08651193066dd5",
            "883471bd025f4d328b6e493cbc129529",
            "aae1cd3fa0ed40d1bfa50636f0f1cb8c",
            "3fc2d757a91a4d0fb57d120f83217670",
            "43bb225a077d48288c757e76a6136a63",
            "5ff829f89e50495fbbfd95231a2af8cc",
            "93bee8175cda498ea7999afcc32317ad",
            "09b0f2ad6c8643e89cb3426bd18388a3",
            "7b1dfee284974a2d903f807a4cda3392",
            "648934cfd4354e5aa8bf4477e798165c",
            "867148449c6c48569575b9e3700d13f3",
            "2e80578397b54187922758c96a3e16f1",
            "612fa8cdff6345a7ba957584876df766",
            "711b319615a143aca0d4735aae65aa57",
            "466cd25125d94b8a893f0412a85da96b",
            "338f19b23cce419f884577e0bdbaacb8"
          ]
        },
        "id": "G1bX4lGAO_d9",
        "outputId": "ddaa2ff9-c185-4f0b-8897-66fe01eaaa0c"
      },
      "source": [
        "from transformers import AutoFeatureExtractor\n",
        "\n",
        "feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint)\n",
        "feature_extractor"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2fafd1b4c6c5483f9922bef1395dba65",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.80k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/configuration_utils.py:337: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n",
            "  \"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 \"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5ff829f89e50495fbbfd95231a2af8cc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/159 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Wav2Vec2FeatureExtractor {\n",
              "  \"do_normalize\": true,\n",
              "  \"feature_extractor_type\": \"Wav2Vec2FeatureExtractor\",\n",
              "  \"feature_size\": 1,\n",
              "  \"padding_side\": \"right\",\n",
              "  \"padding_value\": 0.0,\n",
              "  \"return_attention_mask\": false,\n",
              "  \"sampling_rate\": 16000\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-zYhYOuDL9XP"
      },
      "source": [
        "As we've noticed earlier, the samples are roughly 1 second long, so let's set it here:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wX35xSLx-ghC"
      },
      "source": [
        "max_duration = 1.0  # seconds"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qUtxmoMvqml1"
      },
      "source": [
        "We can then write the function that will preprocess our samples. We just feed them to the `feature_extractor` with the argument `truncation=True`, as well as the maximum sample length. This will ensure that very long inputs like the ones in the `_silence_` class can be safely batched."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4O_p3WrpRyej"
      },
      "source": [
        "def preprocess_function(examples):\n",
        "    audio_arrays = [x[\"array\"] for x in examples[\"audio\"]]\n",
        "    inputs = feature_extractor(\n",
        "        audio_arrays, \n",
        "        sampling_rate=feature_extractor.sampling_rate, \n",
        "        max_length=int(feature_extractor.sampling_rate * max_duration), \n",
        "        truncation=True, \n",
        "    )\n",
        "    return inputs"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OlbYCcVZ4hHu"
      },
      "source": [
        "The feature extractor will return a list of numpy arays for each example:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGUvcAlAqKy-",
        "outputId": "73052908-db3e-48e7-f7a9-cac4ae016535"
      },
      "source": [
        "preprocess_function(dataset['train'][:5])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_values': [array([-9.1631009e-05, -9.1631009e-05, -9.1631009e-05, ...,\n",
              "       -4.6719767e-02, -8.0353022e-01, -1.3182331e+00], dtype=float32), array([0.01049979, 0.01049979, 0.01049979, ..., 0.6454253 , 0.43378347,\n",
              "       0.25741526], dtype=float32), array([ 9.0340059e-04,  9.0340059e-04,  9.0340059e-04, ...,\n",
              "       -1.7281245e-01,  2.2313449e-01,  1.9931581e+00], dtype=float32), array([ 1.5586768 ,  0.3870289 ,  0.74101615, ..., -0.8897349 ,\n",
              "       -0.7703889 , -0.09471782], dtype=float32), array([-0.01518929, -0.01518929, -0.01518929, ..., -0.84138   ,\n",
              "        0.22227868, -0.02409434], dtype=float32)]}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1Nw4FwN5wCS"
      },
      "source": [
        "To apply this function on all utterances in our dataset, we just use the `map` method of our `dataset` object we created earlier. This will apply the function on all the elements of all the splits in `dataset`, so our training, validation and testing data will be preprocessed in one single command."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405,
          "referenced_widgets": [
            "7e4fb53a3bd24f7f82ca87cdd578419d",
            "7bf541a338b54a61bf29933ab361c315",
            "1ce93ae0105540ccbbc65edc62dfacb4",
            "a9ad24f133d147d082ea356331dc85a2",
            "32c1022fdf9c41a8b5ca82f84dd79a6e",
            "6bec4cc7d74343419615172854132ee4",
            "963ff74cca7a41279be689675195524c",
            "a0d5f199888b47ab864e2bd9f5476191",
            "3e9f2f257e954801bae9b5fd4b399146",
            "ff67c79ff52e480baa64510195818dea",
            "d743ae8ac06a41668cdda8e66e8db4a4",
            "e423a8f9cbe94a2db9be2315ec331ed6",
            "b971ff42d53f4e8f8a9ff54df290dba4",
            "45bb211acb2d4e968bd0fcc6fcfa68cc",
            "47a65edad06e4103a01c7c824fdc0128",
            "26e65879797148319bec3d75f6c1c0ea",
            "6df3284c58ba4e56ac6665f9dd04bfec",
            "8a8afbbd699c43b1a1d1b0c9a73e30da",
            "5d318e9c5eae4dee9e13eb0300dc06c6",
            "9b386647f3924128ac163737a7ca40c7",
            "55d11761df614dc0a0af3a22e805f2fd",
            "33750f20475e43b0ad402d07b256f7fe",
            "e564387d8dcd469b8dea5014ce62fe67",
            "18a2f7b99c7e4423b2e90d0dcdb04192",
            "d31b4ac27f5b4baea7f8414b1c709b80",
            "905d95d206e6414293269d50ac52c699",
            "f4a16fbb6ff648b2ac65f01c439c8469",
            "f3ee953b9aa34f449b1508f72effd0b9",
            "fee49bab248448c1b3e2cf70d707184a",
            "040d8af1869c42f492fb11b69835f020",
            "e7f4a64e4abb4b94bed9d18df72d04df",
            "13d1759849494181b82925ea9dc95356",
            "cbee1e19283e48d39b90fcc4a498aa30"
          ]
        },
        "id": "FfxFgJ_6qPPy",
        "outputId": "aedec9cc-a9c1-403e-f052-90efea5fb14c"
      },
      "source": [
        "encoded_dataset = dataset.map(preprocess_function, remove_columns=[\"audio\", \"file\"], batched=True)\n",
        "encoded_dataset"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e4fb53a3bd24f7f82ca87cdd578419d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/52 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e423a8f9cbe94a2db9be2315ec331ed6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/7 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e564387d8dcd469b8dea5014ce62fe67",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_values', 'label'],\n",
              "        num_rows: 51094\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['input_values', 'label'],\n",
              "        num_rows: 6798\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_values', 'label'],\n",
              "        num_rows: 3081\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0OwX3Es580y"
      },
      "source": [
        "Even better, the results are automatically cached by the 🤗 Datasets library to avoid spending time on this step the next time you run your notebook. The 🤗 Datasets library is normally smart enough to detect when the function you pass to map has changed (and thus requires to not use the cache data). 🤗 Datasets warns you when it uses cached files, you can pass `load_from_cache_file=False` in the call to `map` to not use the cached files and force the preprocessing to be applied again."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOXmyPQ76Qv9"
      },
      "source": [
        "### Training the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0a-2YT7O6ayC"
      },
      "source": [
        "Now that our data is ready, we can download the pretrained model and fine-tune it. For classification we use the `AutoModelForAudioClassification` class. Like with the feature extractor, the `from_pretrained` method will download and cache the model for us. As the label ids and the number of labels are dataset dependent, we pass `num_labels`, `label2id`, and `id2label` alongside the `model_checkpoint` here:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9DDujL0q1ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188,
          "referenced_widgets": [
            "facdfa8c9711409dbc854ba83c77f81f",
            "a7fa4d0f51394388886d867c4353544b",
            "1f33066c8136472986ab2edb836a8d0a",
            "750e60cb4444418aabed9459878eab9b",
            "70c3bd95dd014db09c101f1e0d02fd94",
            "eb7397f36f2e4a74b91e0d8eb88822be",
            "033f0a2fd78142729c5d52c9a43fa9a3",
            "d44649ee285e4675b431ae6585423724",
            "c229fc03e52a4ddcb22bdedee8c9b663",
            "f6547339d953418a995c494c13e07321",
            "d0fc1a8e569346cfaca7343b3a0a27d6"
          ]
        },
        "outputId": "a3575a2d-1c4f-4f28-b3d7-6d59812f045f"
      },
      "source": [
        "from transformers import AutoModelForAudioClassification, TrainingArguments, Trainer\n",
        "\n",
        "num_labels = len(id2label)\n",
        "model = AutoModelForAudioClassification.from_pretrained(\n",
        "    model_checkpoint, \n",
        "    num_labels=num_labels,\n",
        "    label2id=label2id,\n",
        "    id2label=id2label,\n",
        ")\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/configuration_utils.py:337: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n",
            "  \"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 \"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "facdfa8c9711409dbc854ba83c77f81f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/363M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at facebook/wav2vec2-base were not used when initializing Wav2Vec2ForSequenceClassification: ['project_q.weight', 'quantizer.weight_proj.bias', 'project_hid.weight', 'project_q.bias', 'project_hid.bias', 'quantizer.codevectors', 'quantizer.weight_proj.weight']\n",
            "- This IS expected if you are initializing Wav2Vec2ForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing Wav2Vec2ForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of Wav2Vec2ForSequenceClassification were not initialized from the model checkpoint at facebook/wav2vec2-base and are newly initialized: ['projector.bias', 'classifier.weight', 'projector.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8EmET_f6458"
      },
      "source": [
        "The warning is telling us we are throwing away some weights (the `quantizer` and `project_q` layers) and randomly initializing some other (the `projector` and `classifier` layers). This is expected in this case, because we are removing the head used to pretrain the model on an unsupervised Vector Quantization objective and replacing it with a new head for which we don't have pretrained weights, so the library warns us we should fine-tune this model before using it for inference, which is exactly what we are going to do."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEfyuq1U8hDT"
      },
      "source": [
        "To instantiate a `Trainer`, we will need to define the training configuration and the evaluation metric. The most important is the [`TrainingArguments`](https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments), which is a class that contains all the attributes to customize the training. It requires one folder name, which will be used to save the checkpoints of the model, and all other arguments are optional:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xc_MTm0Ks3DF"
      },
      "source": [
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "\n",
        "args = TrainingArguments(\n",
        "    f\"{model_name}-finetuned-ks\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    save_strategy = \"epoch\",\n",
        "    learning_rate=3e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    gradient_accumulation_steps=4,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=5,\n",
        "    warmup_ratio=0.1,\n",
        "    logging_steps=10,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"accuracy\",\n",
        "    push_to_hub=True,\n",
        ")"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xi6JYNYs8lJO"
      },
      "source": [
        "Here we set the evaluation to be done at the end of each epoch, tweak the learning rate, use the `batch_size` defined at the top of the notebook and customize the number of epochs for training, as well as the weight decay. Since the best model might not be the one at the end of training, we ask the `Trainer` to load the best model it saved (according to `metric_name`) at the end of training.\n",
        "\n",
        "The last argument `push_to_hub` allows the Trainer to push the model to the [Hub](https://huggingface.co/models) regularly during training. Remove it if you didn't follow the installation steps at the top of the notebook. If you want to save your model locally with a name that is different from the name of the repository, or if you want to push your model under an organization and not your name space, use the `hub_model_id` argument to set the repo name (it needs to be the full name, including your namespace: for instance `\"anton-l/wav2vec2-finetuned-ks\"` or `\"huggingface/anton-l/wav2vec2-finetuned-ks\"`)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VE_HSha9RZk"
      },
      "source": [
        "Next, we need to define a function for how to compute the metrics from the predictions, which will just use the `metric` we loaded earlier. The only preprocessing we have to do is to take the argmax of our predicted logits:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVWfiBuv2uCS"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    \"\"\"Computes accuracy on a batch of predictions\"\"\"\n",
        "    predictions = np.argmax(eval_pred.predictions, axis=1)\n",
        "    return metric.compute(predictions=predictions, references=eval_pred.label_ids)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTF0dWw49fB9"
      },
      "source": [
        "Then we just need to pass all of this along with our datasets to the `Trainer`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McVoaCPr3Cj-"
      },
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=encoded_dataset[\"train\"],\n",
        "    eval_dataset=encoded_dataset[\"validation\"],\n",
        "    tokenizer=feature_extractor,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltokP9mO9pjI"
      },
      "source": [
        "You might wonder why we pass along the `feature_extractor` as a tokenizer when we already preprocessed our data. This is because we will use it once last time to make all the samples we gather the same length by applying padding, which requires knowing the model's preferences regarding padding (to the left or right? with which token?). The `feature_extractor` has a pad method that will do all of this for us, and the `Trainer` will use it. You can customize this part by defining and passing your own `data_collator` which will receive the samples like the dictionaries seen above and will need to return a dictionary of tensors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9j6VNsGP97LG"
      },
      "source": [
        "Now we can finetune our model by calling the `train` method:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Pps61vF_4QaH",
        "outputId": "fc32c7ba-c478-4725-d06a-fc8ba54ddff0"
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running training *****\n",
            "  Num examples = 51094\n",
            "  Num Epochs = 5\n",
            "  Instantaneous batch size per device = 32\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 128\n",
            "  Gradient Accumulation steps = 4\n",
            "  Total optimization steps = 1995\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1995' max='1995' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1995/1995 1:11:18, Epoch 4/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.790800</td>\n",
              "      <td>0.677599</td>\n",
              "      <td>0.900853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.320200</td>\n",
              "      <td>0.206119</td>\n",
              "      <td>0.976317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.221000</td>\n",
              "      <td>0.125691</td>\n",
              "      <td>0.978523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.177300</td>\n",
              "      <td>0.099014</td>\n",
              "      <td>0.981318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.172900</td>\n",
              "      <td>0.095161</td>\n",
              "      <td>0.982348</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 6798\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-399\n",
            "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-399/config.json\n",
            "Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-399/pytorch_model.bin\n",
            "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-399/preprocessor_config.json\n",
            "Configuration saved in wav2vec2-base-finetuned-ks/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 6798\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-798\n",
            "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-798/config.json\n",
            "Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-798/pytorch_model.bin\n",
            "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-798/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 6798\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-1197\n",
            "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1197/config.json\n",
            "Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-1197/pytorch_model.bin\n",
            "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1197/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 6798\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-1596\n",
            "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1596/config.json\n",
            "Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-1596/pytorch_model.bin\n",
            "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1596/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 6798\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-1995\n",
            "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1995/config.json\n",
            "Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-1995/pytorch_model.bin\n",
            "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1995/preprocessor_config.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from wav2vec2-base-finetuned-ks/checkpoint-1995 (score: 0.9823477493380406).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1995, training_loss=0.5248636143846919, metrics={'train_runtime': 4281.3853, 'train_samples_per_second': 59.67, 'train_steps_per_second': 0.466, 'total_flos': 2.31918157475328e+18, 'train_loss': 0.5248636143846919, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vyb-58x_-A0e"
      },
      "source": [
        "We can check with the `evaluate` method that our `Trainer` did reload the best model properly (if it was not the last one):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "niniUAnb5IrR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "306c07b3-e482-4c62-a147-93fb6e16e4da"
      },
      "source": [
        "trainer.evaluate()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 6798\n",
            "  Batch size = 32\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='213' max='213' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [213/213 01:07]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'epoch': 5.0,\n",
              " 'eval_accuracy': 0.9823477493380406,\n",
              " 'eval_loss': 0.09516120702028275,\n",
              " 'eval_runtime': 68.0133,\n",
              " 'eval_samples_per_second': 99.951,\n",
              " 'eval_steps_per_second': 3.132}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ymwN-SIR-NDF"
      },
      "source": [
        "You can now upload the result of the training to the Hub, just execute this instruction:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4aNMErFz-GzX"
      },
      "source": [
        "trainer.push_to_hub()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZQnNUsI-Q4S"
      },
      "source": [
        "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier `\"your-username/the-name-you-picked\"` so for instance:\n",
        "\n",
        "```python\n",
        "from transformers import AutoModelForAudioClassification, AutoFeatureExtractor\n",
        "\n",
        "feature_extractor = AutoFeatureExtractor.from_pretrained(\"anton-l/my-awesome-model\")\n",
        "model = AutoModelForAudioClassification.from_pretrained(\"anton-l/my-awesome-model\")\n",
        "\n",
        "```"
      ]
    }
  ]
}